Accurate traffic light detection and classification are fundamental for autonomous vehicle (AV) navigation and real-time traffic management in complex urban environments. Existing systems often fall short of reliably identifying and classifying traffic light states in real-time, including their flashing modes. This study introduces FlashLightNet, a novel end-to-end deep learning framework that integrates the nano version of You Only Look Once, version 10m (YOLOv10n) for traffic light detection, Residual Neural Networks 18 (ResNet-18) for feature extraction, and a Long Short-Term Memory (LSTM) network for temporal state classification. The proposed framework is designed to robustly detect and classify traffic light states, including conventional signals (red, green, and yellow) and flashing signals (flash red and flash yellow), under diverse and challenging conditions such as varying lighting, occlusions, and environmental noise. The framework has been trained and evaluated on a comprehensive custom dataset of traffic light scenarios organized into temporal sequences to capture spatiotemporal dynamics. The dataset has been prepared by taking videos of traffic lights at different intersections of Starkville, Mississippi, and Mississippi State University, consisting of red, green, yellow, flash red, and flash yellow. In addition, simulation-based video datasets with different flashing rates—2, 3, and 4 s—for traffic light states at several intersections were created using RoadRunner, further enhancing the diversity and robustness of the dataset. The YOLOv10n model achieved a mean average precision (mAP) of 99.2% in traffic light detection, while the ResNet-18 and LSTM combination classified traffic light states (red, green, yellow, flash red, and flash yellow) with an F1-score of 96%.

Reliable perception of roadway signals is critical for autonomous vehicles operating in complex urban environments, particularly when traffic lights convey safety-critical instructions through flashing and arrow indications that extend beyond conventional red, yellow, and green states. However, most existing vision-based approaches focus primarily on static traffic-light recognition and lack robust mechanisms for interpreting temporal behaviors such as flashing signals. To address this limitation, this paper proposes a unified real-time perception framework, termed HybridSignalNet, for multi-class recognition of traffic lights, road signs, and lane-related roadway elements. The framework combines spatial detection with temporal state reasoning to interpret both steady and flashing signal patterns in video streams. Experimental evaluation demonstrates strong performance across multiple object classes, achieving an average detection F1-score of 91.3%, while traffic-light state classification reaches 96.7%, including reliable identification of flashing and arrow-based signals. The proposed system operates in real-time and provides an interpretable and deployable solution for intelligent transportation systems and autonomous driving applications, particularly at signalized intersections where temporal signal understanding is essential for safe decision-making.